ICEG Morphology Classification using an Analogue VLSI Neural Network

An analogue VLSI neural network has been designed and tested to perform cardiac morphology classification tasks. Analogue tech(cid:173) niques were chosen to meet the strict power and area requirements of an Implantable Cardioverter Defibrillator (ICD) system. The ro(cid:173) bustness of the neural network architecture reduces the impact of noise, drift and offsets inherent in analogue approaches. The net(cid:173) work is a 10:6:3 multi-layer percept ron with on chip digital weight storage, a bucket brigade input to feed the Intracardiac Electro(cid:173) gram (ICEG) to the network and has a winner take all circuit at the output. The network was trained in loop and included a commercial ICD in the signal processing path.